AI Shaping: Turning General-Purpose AI into Productive AI Work

This paper defines AI shaping as the operating-model discipline for turning broad general-purpose AI capability into productive AI work by establishing shaped intelligence that carries a reusable and iteratively improvable domain work pattern and reusable work basis across recurring work. The opportunity exists where work retains stable-enough underlying structure despite changing subjects, inputs and work state. The operational effect and principal capability-evidence signal is two-sided work-burden shift: moving more domain-practice and subject-context burden, and more work-state and resumption burden, from the responsible person to shaped intelligence while preserving source basis and the human-authority boundary. Shaped intelligence is the reusable domain-facing capability established through AI shaping. It carries the domain work pattern and work basis into recurring work. Under the direct-shaping approach, the discipline establishes bounded shaped intelligence without first materialising separately reusable AI-shaping intelligence. Under the mediated-shaping approach, separately established AI-shaping intelligence carries the reusable shaping pattern used to develop, audit, improve, adapt, extend, transfer-evaluate or materially renew shaped intelligence under human direction. The paper explains the value hierarchy, work burden and work-burden shift, the direct- and mediated-shaping approaches, concept architecture, category fit, a public-safe capability lifecycle, domain extension and transfer, adjacent-framework positioning and protected-method limits. It supports category understanding only. The related evidence papers carry their own bounded evidence objects and limitations.